Truncated robust distance for clinical laboratory safety data monitoring and assessment.

Details

Serval ID
serval:BIB_48B22768AE99
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Truncated robust distance for clinical laboratory safety data monitoring and assessment.
Journal
Journal of Biopharmaceutical Statistics
Author(s)
Lin X., Parks D., Zhu L., Curtis L., Steel H., Rut A., Mooser V., Cardon L., Menius A., Lee K.
ISSN
1520-5711 (Electronic)
ISSN-L
1054-3406
Publication state
Published
Issued date
2012
Peer-reviewed
Oui
Volume
22
Number
6
Pages
1174-1192
Language
english
Notes
Publication types: Journal Article
Abstract
Laboratory safety data are routinely collected in clinical studies for safety monitoring and assessment. We have developed a truncated robust multivariate outlier detection method for identifying subjects with clinically relevant abnormal laboratory measurements. The proposed method can be applied to historical clinical data to establish a multivariate decision boundary that can then be used for future clinical trial laboratory safety data monitoring and assessment. Simulations demonstrate that the proposed method has the ability to detect relevant outliers while automatically excluding irrelevant outliers. Two examples from actual clinical studies are used to illustrate the use of this method for identifying clinically relevant outliers.
Pubmed
Web of science
Create date
10/01/2013 11:23
Last modification date
20/08/2019 13:55
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